A Comparative Study of Image...

Spatial Retargeting Diego Gutierrez

Universidad de Zaragoza

(slides material also from Miki Rubinstein, Olga Sorkine,

Arik Shamir and Susana Castillo)

The Retargeting Problem

Common solutions

• Homogeneous squeezing/stretching

• Cropping [DeCarlo and Santella 2002; Viola and Jones 2004…]

• Hybrid solution [modern TV sets]

original squeeze crop hybrid

Visual Media Retargeting: Siggraph Asia Course 2009

Ariel Shamir

The Interdisciplinary Center, Herzliya

Olga Sorkine

New York Univeristy

Visual Media Retargeting: An Example

[Avidar & Shamir 2007]

Visual Media Retargeting: Scaling


Visual Media Retargeting: Seams


1. Define an energy function E(I)

(interest, importance, saliency…)

2. Use some operator(s)

to change the image I

Visual Media Retargeting: Energy Concept


Visual Media Retargeting: Energy & Saliency

• Magnitude of gradients (simple)

• Saliency (e.g. Itty’s method) - multires

[Shamir and Sorkine 2009]

Different energy functions


•Histogram of Gradients

•Entropy

•E1

•Mean Shift & E1



•Entropy

•E1

•Mean Shift & E1


Simple operators: cropping


• Crop s.t. important (salient) parts remain

• Use domain-specific tools, such as face

detector, gaze estimation… [DeCarlo and

Santella 2002; Viola and Jones 2004]

Simple operators: scaling


• Cam combine with cropping techniques (done

on modern TV sets – center remains, peripheral

data is scaled)

• Distorts content but is perfectly temporally

coherent (video)

Discrete vs continuous


Problem statement

• Given an image I of size (n x m), we want to produce an image I* of size (n* x m*) which is a good representative of image I

• But what is a “good representative”? No definitions exist

• Goals of a retargeting algorithm: – 1. The important content of I should be preserved in I*.

– 2. The important structure of I should be preserved in I*.

– 3. I* should be artifact-free

Discrete approaches


• Seam carving for content aware image resizing

SIGGRAPH 2007

S. Avidan and A. Shamir

• Improved seam carving for video retargeting

SIGGRAPH 2008

M. Rubinstein, A. Shamir and S. Avidan

• Seam carving for Media Retargeting

Trans. Of the ACM

S. Avidan and A. Shamir

• Multi-Operator Media Retargeting

SIGGRAPH 2009

M. Rubinstein, A. Shamir and S. Avidan

Continuous approaches


• Feature-aware textureing

EGSR 2006

R. Gal, O. Sorkine and D. Cohen-Or

• Non-homogeneous content-drive video retargeting

ICCV 2007

L. Wolf, M Guttmann and D. Cohen-Or

• Optimized scale-and-stretch for image resizing

SIGGRAPH ASIA 2008

Y. Wang, C. Tai, O. Sorkine and T. Lee

• Shrinkability maps for content-aware video resizing

Pacific Graphics 2008

Y. Zhang, S. Hu and R. Martin

Discrete example: Seam carving

[Rubinstein, Avidan and Shamir 2007]

Seam carving


Seam carving: problems


• Discrete and greedy – may break structures

• Can run out of good seams in one direction

Continuous example: Warping

[Wang, Tai, Sorkine and Lee 2008]

• Allow important regions to uniformly scale

• Find optimal local scaling factors by global

optimization

• Result: preserve the shape of important regions,

distort non-important ones

Continuous example: Warping

[Wang, Tai, Sorkine and Lee 2008]

• Grid mesh, preserve the shape of the important

quads

• Optimize the location of mesh vertices,

interpolate image

Video?


• Naïve… every frame by itself

Video?


• Camera movement

• Object movement

• Seams should adapt and change through time!

• Global Solution (video cube)

Video?


Current State of Retargeting Research

No clear evaluation methodology!

– Mostly visual comparison

– Small subset of previous techniques

Relation between the operator and the type of content?

Tradeoff between loss of content and deformation?

Is there an agreement between viewers on retargeting

evaluation?

Computational retargeting measure?

Source

[Rubinstein, Gutierrez, Sorkine and Shamir 2010]

• Benchmark and evaluation methodology for image retargeting

• Comprehensive perceptual study and analysis of image retargeting

http://people.csail.mit.edu/mrub/retargetme/

A Comparative Study of Image Retargeting Miki Rubinstein, Diego Gutierrez, Olga Sorkine and Arik Shamir

ACM Transactions on Graphics, Vol. 29(5) (SIGGRAPH Asia 2010)


Goals

• What is the “correct” way to retarget this image?


Goals

• The dataset and user study

• User response (subjective) analysis

– Is there consensus between viewers?

– When is one method better than another?

• Computational (objective) analysis

– Can an image distance measure predict retargeting quality?


Constructing the Dataset

• Image Retargeting objectives:

1. Preserve the important content and structures

2. Limit artifacts

• Concentrate on challenging images from previous

publications

• Classified by attributes

– people/faces, line/edges, foreground objects, texture, geometric

structures, symmetry


• Seam Carving [SC] [Rubinstein et al. 2008]

• Shift Map [SM] [Pritch et al. 2009]

• Multi-Operator [MULTIOP] [Rubinstein et al. 2009]

• Warping [WARP] [Wolf et al. 2007]

• Streaming Video [SV] [Krähenbühl et al. 2009]

• Scale-and-Stretch [SNS] [Wang et al. 2008]

• Cropping [CR] [Manual]

• Scaling [SCL] [Cubic interpolation]

Retargeting Operators D

iscre

te

Contin

uous

Refe

rence


Comparative Analysis


The Survey Interface

Retargeting Results


Additional Questions


User Statistics

• Each participant performs 12 comparisons over 5

images

• 210 participants; 252 votes per image

– Half volunteers

– Half (25 cents per completed survey)

• Average time to complete: 20 minutes

“It was a very interesting survey. Very nice experience”

“i need your more survey so that i can help u a lot”


User Statistics

Gender Age

Country

Graphics Background Comparison time


User Agreement

• Similarity of votes = consensus on “good” retargeting

• Coefficient of Agreement [Kendall 1940]

• aij = # times method i chosen over method j

• m = # participants

• t = 8 (# retargeting operators)

•


User Agreement

• Low agreement in general

• Greater agreement on images containing faces/people, evident foreground objects and symmetry.

lines/

edges

faces/

people

Textur

e

foregroun

d

objects

Geometri

c

Structure

s

Symmetr

y

Total

u 0.073 0.166 0.070 0.146 0.084 0.132 0.095

lines/

edges

faces/

people

Textur

e

foregroun

d

objects

Geometri

c

Structure

s

Symmetr

y

Total

u 0.073 0.166 0.070 0.146 0.084 0.132 0.095

lines/

edges

faces/

people

Textur

e

foregroun

d

objects

Geometri

c

Structure

s

Symmetr

y

Total

u 0.073 0.166 0.070 0.146 0.084 0.132 0.095


Operator Ranking

Lines/edges

Total

Faces/people

Nonhomo. Warping

Streaming Video

Cropping Multi- operator

Shift- maps

Scale & Stretch

Scaling Seam Carving

Operator Ranking

By Attribute

Total

Rank

1 2 3 4 5 6 7 8


Additional Questions

Cropping Shift-maps Scale & Stretch

(At least for our retargeted setup)

SUBJECTIVE:

Clear and consistent division in groups

CR, SV, MULTIOP: good!

SCL, SC, WARP: not so good

Greater agreement for faces/people and foreground objects:

Saliency at object level?

Partial Conclusion

Source is Usually Unknown!


• Similar setup, source image not shown

• New set of 210 participants


“No Reference” Experiment Results

“No Reference” Experiment Results

Reference No Reference

Nonhomo. Warping

Streaming Video

Cropping Multi- operator

Shift- maps

Scale & Stretch

Scaling Seam Carving


Analysis of the users’ responses:

significance test

Computational Retargeting Measures

• Goal: can computational image distance measures predict human retargeting preferences?

– Can be used to evaluate new operators

– Can be used to develop new operators – [Simakov et al. 2008], [Rubinstein et al. 2009]


(Non-blind) Retargeting Measures

,

= score

Source Retarget

?

?

Objective Measures

• High level semantics: – Bidirectional Similarity [BDS] - Simakov et al. 2008

– Bidirectional Warping [BDW] - Rubinstein et al. 2009

– SIFT Flow [SIFTflow] – Liu et al. 2008

– Earth Mover’s Distance [EMD] - Pele and Werman 2009

• Low level features – Edge Histogram [EH] – Menjunath et al. 2001

– Color Layout [CL] – Kasutani and Yamada 2001

• See dataset website and supplemental material for more details


How to Evaluate an Objective Measure?

• Define rate of agreement as the correlation between

rankings induced by the user responses, and the

objective measure Subjective Objective

Objective Analysis Results

• The results were spectacular(ly poor!)

• We tried something else: – SIFT-flow [Liu et al. 2008]: SIFT

– Earth mover’s distance [Pele & Werman 2009]: EMD

• Somewhat better

Metric lines/ edges

faces/ people

texture Foreground

objects geometric structures

symmetry total

BDS 0.04 0.19 0.06 0.17 0.00 -0.01 0.08

BDW 0.03 0.05 -0.05 0.06 0.00 0.12 0.05

EH 0.04 -0.08 -0.06 -0.08 0.10 0.30 0.00

CL -0.02 -0.18 -0.07 -0.18 -0.01 0.21 -0.07

SIFTflow 0.10 0.25 0.12 0.22 0.08 0.07 0.14 EMD 0.22 0.26 0.11 0.23 0.24 0.50 0.25

Metric lines/ edges

faces/ people

texture Foreground


symmetry total

BDS 0.04 0.19 0.06 0.17 0.00 -0.01 0.08

BDW 0.03 0.05 -0.05 0.06 0.00 0.12 0.05

EH 0.04 -0.08 -0.06 -0.08 0.10 0.30 0.00

CL -0.02 -0.18 -0.07 -0.18 -0.01 0.21 -0.07

SIFTflow 0.10 0.25 0.12 0.22 0.08 0.07 0.14 EMD 0.22 0.26 0.11 0.23 0.24 0.50 0.25

Metric lines/ edges

faces/ people

texture Foreground


symmetry total

BDS 0.04 0.19 0.06 0.17 0.00 -0.01 0.08

BDW 0.03 0.05 -0.05 0.06 0.00 0.12 0.05

EH 0.04 -0.08 -0.06 -0.08 0.10 0.30 0.00

CL -0.02 -0.18 -0.07 -0.18 -0.01 0.21 -0.07

SIFTflow 0.10 0.25 0.12 0.22 0.08 0.07 0.14 EMD 0.22 0.26 0.11 0.23 0.24 0.50 0.25


Can computational image distance metrics predict human

retargeting perception?


SUBJECTIVE:

More recent algorithms do outperform their predecessors in

a (surprisingly) consistent way

Cropping is the simplest and one of the best:

loss of info OK

distortion NOT OK

bring it back!

Interestingly, scaling and seam carving do not do very well

on their own… but are two of the three in MULTIOP:

combination of simple methods?

Conclusions

Conclusions

OBJECTIVE:

We are a long way from predicting human perception

Four similarity image metrics did not perform well at all

Two metrics not originally designed for that purpose did

somewhat better

Optimize retargeting wrt those?

Further research is (badly!) needed

Conclusions

Conclusions

We need video analysis and experiments!

http://people.csail.mit.edu/mrub/retargetme/

Image Retargeting Quality Assessment Computer Graphics Forum, 2011, Vol. 30, No. 2, Eurographics 2011,

Yong-Jin Liu, Xi Luo, Yu-Ming Xuan, Wen-Feng Chen, Xiao-Lan Fu

Using Eye-Tracking to Assess Different Image Retargeting Methods

Using Eye-Tracking to Assess Different Image Retargeting Methods Susana Castillo,Tilke Judd and Diego Gutierrez

Applied Perception in Graphics and Visualization 2011

[Castillo, Judd and Gutierrez 2011]

Overview


• Seam Carving [SC] [Rubinstein et al. 2008]

• Shift Maps [SM] [Pritch et al. 2009]

• Multi-Operator [MULTIOP] [Rubinstein et al. 2009]

• Streaming Video [SV] [Krähenbühl et al. 2009]

Retargeting Operators

Rank [Rubinstein et al. SIGAsia 2010]

1 2 3 4 5 6 7 8


Collect eye tracking data

Screen resolution

1280x1024

Each image shown for 5 seconds

[Photo Credit: Jason Dorfman CSAIL website]


Eye tracking data

Fixations for 7 users

Contextual guidance of eye movements and attention in real-world scenes: The role of global features on object search [Torralba et al. 2006]


Eye tracking data

Average fixation locations / continuous saliency map


Learning to predict where humans look [Judd et al. 2009]

Eye tracking data

Top 20% salient locations

Learning to predict where humans look [Judd et al. 2009]


• People tend to fixate on: 1. Text & Faces

2. Animals

3. Center

• Features – Low level : illuminance, orientation, color

– Mid level: vanishing point, horizon line

– High level: face detection, object detection

MIT Predictive Model of Saliency

[Judd et al. 2009]

MIT Predictive Model of Saliency

Saliency Maps from eye-tracking data

Saliency Maps predicted by the model from Judd et al. [2009]


Examples and Discussion


Conclusions

• Lots of methods in the past few years, in top-notch places

• Relatively small impact in industry

• We need more (and better!) metrics

• Does video retargeting really work?

http://people.csail.mit.edu/mrub/retargetme/ or Google: “retargetme”

Conclusions

• Eye-tracking data framework

• The model of saliency from Judd et al. [2009] can be an useful tool in a retargeting context when using an eye tracker is not feasible

• Analysis of 4 retargeting operators with 6 image distance measures – Using eye-tracking data can improve the predicting capabilities of these

measures

• Alteration of the image semantics.

– Content removal alters RoIs although the results can be aesthetically pleasing

• Attentional tension between RoIs and artifacts – Large artifacts can remain unnoticed when not in a RoI (At least for our 5

second task)

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